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Abstract Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. Keywords: plant phenotype, high-throughput phenotyping, environmental factor, imaging procedure, data analysis. Introduction Global agricultural demand is expanding rapidly, not the least because of a growing world population but also due to indirect factors which are rendering agricultural production suboptimal, such as unequal food distribution, competing claims for land use and increased demand for meat and dairy due to a change in dietary habits in the G5 countries emerging economics.
Consequences of Environmental Factors for Plant Phenotyping: A Big Challenge for the Imminent Generation Several studies have suggested that upcoming generations can be influenced by the environmental factors experienced by the earlier generation Dawson et al. Importance of Advanced Phenotyping and Phenomics in Modern Agriculture With the rapid development of sequencing technologies, whole genomes of many plant species are now available in online databases.
Mechanism of Imaging Technologies: Meeting Challenges and Needs in Plant Phenomics Imaging and image processing techniques with light sources from visible to near infrared spectrum provide non-destructive plant phenotype image datasets. Available Imaging Devices for High-Throughput Phenotyping Visible Light Imaging In plant science, visible light imaging has been broadly adopted due to its low cost and simplicity. Infrared Imaging Infrared imaging technologies are used for screening objects of internal molecular movements which emit infrared radiation Kastberger and Stachl, Fluorescence Imaging Fluorescence imaging is used from laboratory to field.
Spectroscopy Imaging The use of spectroscopy imaging is very promising for plant phenotyping. Structural Tomography and Other Imaging In recent times, modern optical 3D structural tomography and functional imaging techniques have been developed and extended to improve living plant visualization.
Table 1 Key of imaging techniques and applications purpose. Imaging system Description Phenotypic trait parameters Application purpose Visible light The visible light imaging technique is camera sensitive and produces gray or color scale images. Image-based projected biomass, dynamic growth, color, shape descriptors, root architecture, seed morphology, panicle traits, etc. This imaging technique can be used to assess plant growth status, biomass accumulation, nutritional status, or health status Golzarian et al.
Thermal infrared Thermal infrared imaging sensor includes near-infrared, multispectral line scanning cameras. This imaging technique produces time series or single-time-point analysis based data. Leaf area index, shoot or leaf temperature, surface temperature, insect infestation of grain, leaf and canopy water status, composition parameters for seeds, disease severity, etc.
This imaging technique used to characterize the plant temperature responses to the water status and transpiration rate and detect difference in stomatal conductance of the plant for adoption abiotic stress Chen et al. Fluorescence Fluorescence imaging technique detects chlorophyll and other fluorophores signals using fluorescence cameras. Photosynthetic performance, quantum yield, non-photochemical quenching, leaf disease severity assessments, leaf health status, etc.
It provides a fleet way to probe photosystem status in vivo , diagnosing early stress responses before decline growth Fiorani and Schurr, , useful for disease detection in genetic disease resistance Chen et al. Hyperspectral This imaging technique use hyper spectral, thermal cameras produced continuous, or discrete spectra raw data. Water content, leaf growth and health status, panicle health status, grain quality, pigment composition, etc. This imaging technique used to measure spatiotemporal growth patterns during the experiment and provide insight into the diversity of growth dynamics Chen et al.
Grain quality, tiller, morphometric parameters, water content, flow velocity, etc. This imaging is widely used to asses tissue density Aerts et al. PET Positron emission tomography. Water transport, flow velocity, etc. This is used to visualize distribution and transportation of radionuclide-labeled tracers involved in metabolism-related activities Jahnke et al. MRI Magnetic resonance imaging. Water content, morphometric parameters, etc. The purpose of this imaging technique is to visualize metabolites, provides structural information, and monitor internal physiological processes occurring in vivo Borisjuk et al.
Open in a separate window. Table 2 Image based automated or semi-automated high-throughput plant phenotyping platforms. This is a highly versatile tool that enables large-scale transgenesis and automated high resolution phenotypic plant evolution Reuzeau, This high-throughput platform was developed for automatic screening of rice germplasm resources and populations throughout the growth period and after harvest Yang et al.
Table 3 Open source high-throughput plant phenotype image processing software or tools. Principles of Phenotype Data for Forecasting Plant Performance High-throughput phenotyping provides multi-categorical phenotypic traits, and corresponding trait analysis is essential for the understanding of a stress resistance, b insect and disease resistance and for the c yield and quality improvement Yang et al. Hypothesis Before starting the image data analysis, a proper hypothesis is required that corresponds to the expectation of the experiment within an appropriate statistical framework Vasseur et al.
Data Quality The selection of an inadequate part of a trait often affects the data quality in a negative manner. Data Dimension Phenotypic traits which are extracted from the high-throughput image dataset can be high-dimensional and be highly correlated. Model Selection An appropriate model is needed for phenotypic variance and biomass prediction. Relationship Measurement A bivariate relationship study is a powerful tool that describes numerous relationships among the traits-traits and traits-environment for a given genotype.
Networking Network analysis is also essential to find the relationships among the significant traits. Growth Modeling Another major part of phenotype data analysis is plant modeling Kaitaniemi et al. Classification Classification methods are useful for biological image analysis and have simplified numerous tasks Kamber et al. Conclusion and Future Indication Research in plant biology has benefited and continues to benefit from developing high-throughput traits measurement methodologies at different levels including metabolomics, proteomics, and transcriptomics data Granier and Vile, Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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This imaging technique use hyper spectral, thermal cameras produced continuous, or discrete spectra raw data. Represents specific setups for automated phenotyping, allowing a culture of approximately — Arabidopsis plants in individual pots with automatic watering and imaging system Granier et al. This automated phenotyping platform is an integrated device, allowing simultaneous culture of individual Arabidopsis plants and high-throughput acquisition, storage and analysis of quality phenotypes Tisne et al. This platform was developed to study plant leaf growth fluorescence and root architecture from seedling under control condition for visual phenotyping of large plant populations Walter et al.
High-throughput gene engineering platform developed by Crop Design. This platform monitors plant growth and transpiration rate with stressful environmental condition. This is an automated high-resolution phenomic center which provides non-invasive analysis of plant structure, morphology and function by utilizing cutting edge information technology including high resolution cameras and 3D reconstruction software.
Integrated conveyor and robotic high-throughput plant imaging system for the laboratory, growth chamber and field phenotype screening and phenotyping. A popular, powerful, and extensible application used to process and measure a large quantity of phenotypic traits captured by images. Large-scale plant phenotyping image analysis software for different species based on real-time imaging data obtained from various spectra Klukas et al.
A high-throughput top and side view plant phenotyping image analysis pipeline implemented as a plug-in for ImageJ Hartmann et al. Time-lapse visual, chlorophyll fluorescence, or thermal sequence of image analysis tool for quantification genotype effects of Arabidopsis thaliana , implemented as a plug-in for ImageJ De Vylder et al. Flexible software which simultaneously measures multiple architectural and branching phenotypes from images Crowell et al.
A high-throughput phenotyping tool for plant growth modeling and functional analysis Tessmer et al. A web-based application which measures area-related phenotypic traits from imagery and multiple experimental setup Green et al. Image analysis software for high-throughput phenotyping measurements of seed shape Tanabata et al. Automated hypocotyl growth and shape measuring software from grayscale images of Arabidopsis seedlings Wang et al. Automated leaves image analysis tool which measures a variety of characteristics related to leaf shape and size Bylesjo et al.
An automated software for rapid and large-scale analyses of leaf shape variation Weight et al.